#!/usr/bin/env python3

import sys
import os
sys.path.append(os.path.dirname(os.path.abspath(__file__)))

import pandas as pd
import numpy as np
from datetime import datetime, timedelta

# 测试策略函数
def test_triple_ma_strategy(data: pd.DataFrame, ma5=5, ma10=10, ma20=20, volume_threshold=1.2, **kwargs):
    """三均线策略测试"""
    print(f"测试参数: ma5={ma5}, ma10={ma10}, ma20={ma20}")
    print(f"数据形状: {data.shape}")
    print(f"数据列: {data.columns.tolist()}")
    
    signals = pd.DataFrame(index=data.index, columns=['signal', 'signal_strength'])
    signals['signal'] = 0
    signals['signal_strength'] = 0.0
    
    # 确保参数是整数
    ma5, ma10, ma20 = int(ma5), int(ma10), int(ma20)
    print(f"转换后参数: ma5={ma5}, ma10={ma10}, ma20={ma20}")
    
    # 计算均线
    ma_5 = data['close'].rolling(window=ma5).mean()
    ma_10 = data['close'].rolling(window=ma10).mean()
    ma_20 = data['close'].rolling(window=ma20).mean()
    
    print(f"MA5计算完成，长度: {len(ma_5)}")
    print(f"MA10计算完成，长度: {len(ma_10)}")
    print(f"MA20计算完成，长度: {len(ma_20)}")
    
    # 成交量均线
    vol_ma = data['volume'].rolling(window=20).mean()
    
    signal_count = 0
    for i in range(max(ma5, ma10, ma20), len(data)):
        # 多头排列且放量
        if (ma_5.iloc[i] > ma_10.iloc[i] > ma_20.iloc[i] and
            data['volume'].iloc[i] > vol_ma.iloc[i] * volume_threshold):
            signals.iloc[i, signals.columns.get_loc('signal')] = 1
            signals.iloc[i, signals.columns.get_loc('signal_strength')] = 0.8
            signal_count += 1
        # 空头排列
        elif ma_5.iloc[i] < ma_10.iloc[i] < ma_20.iloc[i]:
            signals.iloc[i, signals.columns.get_loc('signal')] = -1
            signals.iloc[i, signals.columns.get_loc('signal_strength')] = 0.8
            signal_count += 1
    
    print(f"生成信号数量: {signal_count}")
    print(f"信号统计:")
    print(signals['signal'].value_counts())
    
    return signals

if __name__ == "__main__":
    # 创建模拟数据
    dates = pd.date_range(start='2024-01-01', end='2024-12-31', freq='D')
    np.random.seed(42)
    
    # 生成模拟股价数据
    n_days = len(dates)
    base_price = 10.0
    returns = np.random.normal(0.001, 0.02, n_days)  # 0.1%日均收益，2%波动率
    prices = [base_price]
    
    for ret in returns[1:]:
        prices.append(prices[-1] * (1 + ret))
    
    data = pd.DataFrame({
        'date': dates,
        'open': prices,
        'high': [p * (1 + abs(np.random.normal(0, 0.01))) for p in prices],
        'low': [p * (1 - abs(np.random.normal(0, 0.01))) for p in prices],
        'close': prices,
        'volume': np.random.uniform(1000000, 5000000, n_days),
        'pct_chg': [0] + [((prices[i] - prices[i-1]) / prices[i-1] * 100) for i in range(1, len(prices))]
    })
    data.set_index('date', inplace=True)
    
    print("开始测试策略函数...")
    print("=" * 50)
    
    # 测试策略
    result = test_triple_ma_strategy(data, ma5=5, ma10=10, ma20=20, volume_threshold=1.2)
    
    print("=" * 50)
    print("测试完成")